Question 1

import pandas as pd
import sqlite3 as sq3
import numpy as np

df = pd.read_csv("https://raw.githubusercontent.com/bcaffo/ds4bme_intro/master/data/kirby21.csv")

url = "https://raw.githubusercontent.com/bcaffo/MRIcloudT1volumetrics/master/inst/extdata/multilevel_lookup_table.txt"
multilevel_lookup = pd.read_csv(url, sep = "\t").drop(['Level5'], axis = 1)
multilevel_lookup = multilevel_lookup.rename(columns = {
    "modify"   : "roi", 
    "modify.1" : "level4",
    "modify.2" : "level3", 
    "modify.3" : "level2",
    "modify.4" : "level1"})
multilevel_lookup = multilevel_lookup[['roi', 'level4', 'level3', 'level2', 'level1']]

multilevel_lookup

	roi	level4	level3	level2	level1
0	SFG_L	SFG_L	Frontal_L	CerebralCortex_L	Telencephalon_L
1	SFG_R	SFG_R	Frontal_R	CerebralCortex_R	Telencephalon_R
2	SFG_PFC_L	SFG_L	Frontal_L	CerebralCortex_L	Telencephalon_L
3	SFG_PFC_R	SFG_R	Frontal_R	CerebralCortex_R	Telencephalon_R
4	SFG_pole_L	SFG_L	Frontal_L	CerebralCortex_L	Telencephalon_L
...	...	...	...	...	...
278	Chroid_LVetc_L	AnteriorLateralVentricle_L	LateralVentricle_L	Ventricle	CSF
279	Chroid_LVetc_R	AnteriorLateralVentricle_R	LateralVentricle_R	Ventricle	CSF
280	IV_ventricle	IV_ventricle	IV_ventricle	Ventricle	CSF
281	ECCL_L	inf_DPWM_L	InferiorWM_L	WhiteMatter_L	Telencephalon_L
282	ECCL_R	inf_DPWM_R	InferiorWM_R	WhiteMatter_R	Telencephalon_R

283 rows × 5 columns

id = 127
subjectData = pd.read_csv("https://raw.githubusercontent.com/bcaffo/ds4bme_intro/master/data/kirby21.csv")
subjectData = subjectData.loc[(subjectData.type == 1) & (subjectData.level == 5) & (subjectData.id == id)]
subjectData = subjectData[['roi', 'volume']]
## Merge the subject data with the multilevel data
subjectData = pd.merge(subjectData, multilevel_lookup, on = "roi")
subjectData = subjectData.assign(icv = "ICV")
subjectData = subjectData.assign(comp = subjectData.volume / np.sum(subjectData.volume))
subjectData.head()

	roi	volume	level4	level3	level2	level1	icv	comp
0	SFG_L	12926	SFG_L	Frontal_L	CerebralCortex_L	Telencephalon_L	ICV	0.009350
1	SFG_R	10050	SFG_R	Frontal_R	CerebralCortex_R	Telencephalon_R	ICV	0.007270
2	SFG_PFC_L	12783	SFG_L	Frontal_L	CerebralCortex_L	Telencephalon_L	ICV	0.009247
3	SFG_PFC_R	11507	SFG_R	Frontal_R	CerebralCortex_R	Telencephalon_R	ICV	0.008324
4	SFG_pole_L	3078	SFG_L	Frontal_L	CerebralCortex_L	Telencephalon_L	ICV	0.002227

icv = subjectData.groupby(['icv', 'level1'], as_index = False).agg({'volume':'sum'})

source = icv['icv'].to_list()
target = icv['level1'].to_list()
value = icv['volume'].to_list()

dic = {}
index = 1
for s in source:
    if s not in dic:
        dic[s] = index
        index += 1

for t in target:
    if t not in dic:
        dic[t] = index
        index += 1

source_to_idx = []
target_to_idx = []

for s in source:
    source_to_idx.append(dic[s])
for t in target:
    target_to_idx.append(dic[t])

import plotly.graph_objects as go

fig = go.Figure(data=[go.Sankey(
    node = dict(
      pad = 10,
      thickness = 20,
      line = dict(color = "black", width = 0.5),
      label = ['A1', 'A2', 'A3', 'A4', 'A5', 'A6', 'A7', 'A8'],
      color = "blue"
    ),
    link = dict(
        source = source_to_idx, # indices correspond to labels, eg A1, A2, A1, B1, ...
        target = target_to_idx,
        value = value
    )
)])

fig.update_layout(title_text="Sankey Diagram", font_size=15)
fig.show()

Question 2

My webpage :

Question3

import plotly.express as px

con = sq3.connect("opioid.db")

annual = pd.read_sql_query("SELECT * from annual", con)
land = pd.read_sql_query("SELECT * from annual", con)
population = pd.read_sql_query("SELECT * from population", con)

con.close()

Question4

annual.head

<bound method NDFrame.head of              BUYER_COUNTY BUYER_STATE  year count DOSAGE_UNIT countyfips
0          1    ABBEVILLE          SC  2006   877      363620      45001
1          2    ABBEVILLE          SC  2007   908      402940      45001
2          3    ABBEVILLE          SC  2008   871      424590      45001
3          4    ABBEVILLE          SC  2009   930      467230      45001
4          5    ABBEVILLE          SC  2010  1197      539280      45001
...      ...          ...         ...   ...   ...         ...        ...
27753  27754           NA          NV  2007   447      200600         NA
27754  27755           NA          NV  2008     5        2200         NA
27755  27756           NA          OH  2006    23        5100         NA
27756  27757           NA          PR  2006    10       17800         NA
27757  27758           NA          PR  2007     2        1300         NA

[27758 rows x 7 columns]>

annual["DOSAGE_UNIT"] = pd.to_numeric(annual["DOSAGE_UNIT"], downcast="float")

data = annual.groupby('year').mean()

data

	DOSAGE_UNIT
year
2006	2645409.75
2007	2992099.50
2008	3252161.75
2009	3526038.00
2010	3783655.75
2011	4035583.00
2012	3993064.00
2013	3861751.75
2014	3768738.00

fig = px.scatter(x=data.index, y=data['DOSAGE_UNIT'])
fig.show()

My webpage: